Qodo-Embed-1-1.5B
| Property | Value |
|---|---|
| Parameter Count | 1.5B |
| Embedding Dimension | 1536 |
| Max Input Tokens | 32,000 |
| License | QodoAI-Open-RAIL-M |
| Model Hub | Hugging Face |
What is Qodo-Embed-1-1.5B?
Qodo-Embed-1-1.5B is a cutting-edge code embedding model specifically designed for software development retrieval tasks. It represents a significant advancement in code understanding and retrieval capabilities, offering state-of-the-art performance while maintaining a relatively compact model size of 1.5B parameters.
Implementation Details
The model employs advanced transformer architecture optimized for code embeddings, supporting an impressive context window of 32,000 tokens and producing 1536-dimensional embeddings. It requires transformers>=4.39.2 and flash_attn>=2.5.6 for optimal performance.
- Supports multiple programming languages including Python, C++, C#, Go, Java, Javascript, PHP, Ruby, and Typescript
- Optimized for both natural language-to-code and code-to-code retrieval tasks
- Implements efficient token pooling and embedding normalization
- Provides easy integration through both SentenceTransformers and Hugging Face Transformers APIs
Core Capabilities
- Code Search: Enables efficient searching across large codebases
- Retrieval-Augmented Generation (RAG): Enhances code generation with contextual understanding
- Semantic Code Understanding: Captures complex relationships between code snippets
- Multi-Language Support: Processes code from 9 major programming languages
- High-Dimensional Embeddings: Generates rich 1536-dimensional representations
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance on the COIR and MTEB leaderboards while maintaining a smaller parameter count compared to competitors. It combines high accuracy with computational efficiency, making it practical for production deployments.
Q: What are the recommended use cases?
The model excels in code search applications, semantic code understanding, and retrieval-augmented generation systems. It's particularly effective for building developer tools, code search engines, and automated code analysis systems.
